Eliminating prediction bias in CO2 emission models for lactating cows by incorporating feed intake: accurate quantification of methane-reducing effects using a CO2-based method, demonstrated by a case study on 3-nitrooxypropanol

다국어 초록 (Multilingual Abstract)

Objective: The methane (CH₄) emission prediction method, using predicted CO₂ emissions and the CH₄:CO₂ concentration ratio, faces challenges in evaluating the efficacy of CH₄-reducing feed additives due to CO₂ prediction bias associated with energy utilization efficiency. We hypothesized that incorporating dry matter intake (DMI), along with metabolic body weight (MBW) and energy-corrected milk (ECM) as explanatory variables, would reduce this bias. The primary objective was to compare the performance of CO₂ emission models with and without including DMI. The secondary objective was to assess the CO₂-based method’s applicability for quantifying CH₄-reducing effects, through a case study of 3-nitrooxypropanol (3-NOP).Methods: Prediction models for CO₂ emissions were developed including DMI, MBW, and ECM as explanatory variables, based on 219 records obtained from previous experiments with Holstein cows using respiration chambers or headboxes. The model performance was evaluated using cross-validation. Bias associated with energy utilization efficiency was assessed. The applicability of the CO₂-based method to quantify the CH₄-reducing effect of 3-NOP was assessed using data obtained from the literature, including 10 studies with 22 treatment and control mean comparisons. The agreement between the observed and predicted CH₄ reductions was assessed.Results: Combining DMI along with MBW and ECM improved the predictive performance of CO₂ emissions. While the models without DMI showed bias associated with energy utilization efficiency, it was eliminated when DMI was incorporated. Applicability assessment demonstrated that the models without DMI systematically underestimated the CH₄-reducing effect of 3-NOP. In contrast, the models with DMI showed smaller discrepancies between observed and predicted CH₄ reductions.Conclusion: This study highlights the importance of incorporating DMI as an explanatory variable to achieve accurate and unbiased predictions of CO₂ emissions. These findings would contribute to the appropriate application of the CO₂-based method for evaluating the CH₄-reducing effects of feed additives.

다국어 초록 (Multilingual Abstract)

Objective: The methane (CH4 ) emission prediction method, using predicted CO2 emis sions and the CH4 :CO2 concentration ratio, faces challenges in evaluating the efficacy of CH4-reducing feed additives due to CO2 prediction bias associated with energy utilization efficiency. We hypothesized that incorporating dry matter intake (DMI), along with meta bolic body weight (MBW) and energy-corrected milk (ECM) as explanatory variables, would reduce this bias. The primary objective was to compare the performance of CO2

emission models with and without including DMI. The secondary objective was to assess the CO2-based method’s applicability for quantifying CH4-reducing effects, through a case study of 3-nitrooxypropanol (3-NOP).
Methods: Prediction models for CO2 emissions were developed including DMI, MBW, and ECM as explanatory variables, based on 219 records obtained from previous experi ments with Holstein cows using respiration chambers or headboxes. The model perfor mance was evaluated using cross-validation. Bias associated with energy utilization effi ciency was assessed. The applicability of the CO2-based method to quantify the CH4 reducing effect of 3-NOP was assessed using data obtained from the literature, including 10 studies with 22 treatment and control mean comparisons. The agreement between the observed and predicted CH4 reductions was assessed.
Results: Combining DMI along with MBW and ECM improved the predictive perfor mance of CO2 emissions. While the models without DMI showed bias associated with en ergy utilization efficiency, it was eliminated when DMI was incorporated. Applicability as sessment demonstrated that the models without DMI systematically underestimated the CH4-reducing effect of 3-NOP. In contrast, the models with DMI showed smaller discrep ancies between observed and predicted CH4 reductions.
Conclusion: This study highlights the importance of incorporating DMI as an explanatory variable to achieve accurate and unbiased predictions of CO2 emissions. These findings would contribute to the appropriate application of the CO2-based method for evaluating the CH4-reducing effects of feed additives.